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AI-Powered Commerce

AI-Powered Commerce

Andy Pandharikar, Frederik Bussler

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Commerce.AI is a suite of artificial intelligence (AI) tools, trained on over a trillion data points, to help businesses build next-gen products and services. If you want to be the best business on the block, using AI is a must.

Developers and analysts working with AI will be able to put their knowledge to work with this practical guide. You'll begin by learning the core themes of new product and service innovation, including how to identify market opportunities, come up with ideas, and predict trends. With plenty of use cases as reference, you'll learn how to apply AI for innovation, both programmatically and with Commerce.AI. You'll also find out how to analyze product and service data with tools such as GPT-J, Python pandas, Prophet, and TextBlob. As you progress, you'll explore the evolution of commerce in AI, including how top businesses today are using AI. You'll learn how Commerce.AI merges machine learning, product expertise, and big data to help businesses make more accurate decisions. Finally, you'll use the Commerce.AI suite for product ideation and analyzing market trends.

By the end of this artificial intelligence book, you'll be able to strategize new product opportunities by using AI, and also have an understanding of how to use Commerce.AI for product ideation, trend analysis, and predictions.

  • AI-Powered Commerce
  • Contributors
  • About the authors
  • About the reviewer
  • Preface
    • Who this book is for
    • What this book covers
    • To get the most out of this book
    • Download the example code files
    • Download the color images
    • Conventions used
    • Get in touch
    • Share Your Thoughts
  • Section 1:Benefits of AI-Powered Commerce
  • Chapter 1: Improving Market Opportunity Identification
    • Identifying market opportunities the traditional way
    • Big data challenges in market opportunity identification
    • Using AI for market opportunity identification
    • Exploring AI-powered market reports
    • Summary
  • Chapter 2: Creating Product Ideas
    • Understanding the pillars of AI
      • Language understanding
      • Visual understanding
      • Information extraction
      • Information organization
      • Creative AI
    • Why is product ideation so hard?
    • Using Commerce.AI for creative AI
      • Building product ideas
      • Selecting product ideas
      • Iterating product ideas
    • Summary
  • Chapter 3: Understanding How to Predict Industry-Wide Trends Using Big Data
    • Technical requirements
    • Why traditional forecasts fail
      • Its impossible to know everything about all products and services
      • Knowing how products perform today is not a good guide for predicting how they will perform tomorrow
      • The behaviors of many products are highly correlated and can be difficult to disentangle
      • Traditional models get overwhelmed by today's big data
      • The data itself keeps changing
    • Using big data to enable better forecasts
      • Understanding deep learning
      • Learning from examples
      • Demand forecasting with a practical example
      • Sentiment forecasting with a practical example
    • Gaining value from data-driven forecasts
    • Summary
  • Section 2:How Top Brands Use Artificial Intelligence
  • Chapter 4: Applying AI for Innovation Luxury Goods Deep Dive
    • Technical requirements
    • Understanding the challenges of luxury brands
      • Brand management
      • Increasing competition
      • Social media management
      • Matching eccentric customer preferences
      • Understanding unique customer profiles
    • Understanding the data extraction process
      • Tumi uses AI for better marketing
      • Burberry uses AI to improve its clothes
      • Algorithmic couture
      • AI runways
      • RefaceAI
      • Zalando
    • Using Commerce.AI for luxury brands
      • Design and user research
      • Product development and marketing
      • Brand management
      • Trend analysis
    • Summary
  • Chapter 5: Applying AI for Innovation Wireless Networking Deep Dive
    • Technical requirements
    • Understanding the challenges of wireless networking brands
      • Growth in traffic
      • Performance challenges
      • Increasing complexity
      • Sustainability
      • Becoming data-driven
      • 5G
    • Analyzing product data for wireless networking brands
      • Analyzing wireless networking product review data
    • Using Commerce.AI for wireless networking brands
      • Enter data-driven solutions
      • Star ratings
      • Improving best sellers ranking
      • Time compiling weekly reports
      • Improving product sentiment
      • Improving product conversion
      • Search result ranking
      • Detail page glance views
    • Summary
  • Chapter 6: Applying AI for Innovation Consumer Electronics Deep Dive
    • Understanding the challenges faced by consumer electronics brands
      • The needs of the connected consumer
      • A new reality of short-term attention span
      • Meeting the demands of the content consumer
      • The need to become data-driven
      • Emerging consumer electronics markets
    • Analyzing product data for consumer electronics brands
      • Key considerations in the data-driven product strategy
      • How to collect consumer data
      • How to integrate data into the product design
    • Using Commerce.AI for consumer electronics brands
      • Understanding product positioning
      • Analyzing the market with consumer electronics AI reports
      • How does Commerce.AI help with consumer electronics brand research?
      • Generating consumer electronics product ideas
      • Extracting insights from Shopify
      • Sharing insights on Slack
    • Summary
  • Chapter 7: Applying AI for Innovation Restaurants Deep Dive
    • Understanding the challenges of restaurants
      • Profitability
      • Changing guest preferences
      • Creating profitable menus (and pricing)
      • Menu engineering
      • Maintaining online reviews and social media marketing
    • Analyzing product data for restaurants
      • Predicting how food items are likely to perform
      • Predicting how competitors will perform
      • Predicting customer needs based on previous purchases
      • New profile discovery
    • Using Commerce.AI for restaurants
      • Analyzing restaurant customer data
      • Mobile surveys
      • Gauging customer sentiment response based on marketing campaigns
      • Stay connected with your customers
      • Finding and predicting trends in the restaurant business
      • A case study how a large French pizza chain used Commerce.AI
    • Summary
  • Chapter 8: Applying AI for Innovation Consumer Goods Deep Dive
    • Technical requirements
    • Understanding the challenges facing consumer goods brands
      • Competitive consumer goods
      • Consumer goods market intelligence
      • Inventory management
      • Creating the right product mix
      • Creating consumer goods content at scale
      • Consumer goods review analysis
    • Analyzing product data for consumer goods brands
      • Consumer goods content generation
      • Analyze consumer goods reviews
      • Lead time analysis
      • Demand forecasting
      • Maintaining adequate cash flow
      • Analyzing the impact of discounts
      • Identifying seasonal trends
      • Social media analytics
    • Using Commerce.AI for consumer goods brands
      • Measuring product attributes and trends
      • Predicting revenue opportunity
      • Analyzing user personas and customer segments
      • Analyzing the customer journey
      • Generating consumer goods product ideas
    • Summary
  • Section 3:How to Use Commerce.AI for Product Ideation, Trend Analysis, and Predictions
  • Chapter 9: Delivering Insights with Product AI
    • Commerce.AI for product concept and development
      • Market research
      • Understanding demand
      • Product ideation
    • Product launch
      • How AI is changing product launches
      • Predicting demand from early signals
      • AI for the two types of product launches
      • Using AI for product launchesadvantages and disadvantages
    • Product management
      • Tracking product wishes
      • Brand management
      • Using AI for consumer insights
      • Using AI for product tracking
      • Marketing and merchandising
      • Customer support
    • Summary
  • Chapter 10: Delivering Insights with Service AI
    • Empowering your front line
      • Better understanding customer affinities
      • Better understanding purchase reasons
      • Better understanding customer challenges
      • Turning your next interactions into great brand experiences
    • Managing your locations
      • Optimizing your branch
      • Optimizing your employees
      • Optimizing your service
    • Enhancing service offerings
      • Identifying growth areas
      • Leveraging AI for creating stronger service offerings
      • Identifying opportunities to boost customer loyalty
      • Finding new uses for your store
      • Getting a picture of bottlenecks before they escalate
    • Summary
  • Chapter 11: Delivering Insights with Market AI
    • Analyzing trends and white space discovery
      • Improving product idea generation with white spaces
      • The virtualization of everything (VE)
      • Augmented reality
      • E-commerce
      • The rise of social commerce
      • The rise of influencer marketing
      • The gamification of everything
      • The rise of the mass affluent
      • The rise of authenticity
      • Gen Z
      • Demand for sustainable products
    • Connecting market shifts to brands, products, and services
      • Gauging product shifts
      • Recognizing product risk areas
      • Product risk management with AI
    • Understand market DNA
      • Finding market DNA attributes
      • Finding user wishlists and emerging needs with AI
      • AI and consumer-generated content
      • Finding new use contexts with AI
    • Summary
  • Chapter 12: Delivering Insights with Voice Surveys
    • Engaging your customers with ease
      • Product feature prioritization
      • New service offering
      • Post-purchase survey
      • Hotel experience survey
      • Store experience survey
      • Post-call survey
      • Pricing survey
    • Improving your offerings
      • Deriving insights into your existing products
      • How to leverage survey feedback to understand your customers
      • How to act upon those insights
      • Coming up with new product ideas
    • Improving customer loyalty
      • What drives customer loyalty?
    • Summary
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